Cognitive Radio Network
In cognitive radio networks, nodes (transceivers) adapt transmission and reception parameters to communicate efficiently, while avoiding interference with other nodes. The adaptation is based on an active sensing of external and internal radio environments, such as radio frequency (RF) spectrum, and node and network states.
Hidden Nodes
A typical cognitive radio (CR) network includes primary nodes (PNs) and secondary nodes (SNs) that share a broadband RF spectrum. Typically, only the PNs are licensed, and priority access to the RF spectrum. The SNs attempt to aggregate available bandwidth while minimizing interference with or by the PNs nodes.
One problem in CR networks relates to hidden nodes (HNs), which are detectable by some but not all nodes. This leads to difficulties in media access control (MAC). The HN problem needs to be resolved to minimize interference. For this purpose, the SNs should collaboratively sense the spectrum and decide which part of the spectrum is available.
Hidden Node Detection
Conventional collaborative sensing generally involves signaling via a narrow band dedicated control channel (DCC). To solve the hidden node HN problem, one method uses a boosting protocol where nodes in the network broadcast strong signals on frequency bands where the nodes detect PN signals, thus reducing the need for the DCC, Weiss et al., “Efficient Signaling of Spectral Resources in Spectrum Pooling Systems,” Proceedings of the 10th Symposium on Communications and Vehicular Technology (SCVT), November 2003. If the boosting is for a short period of time and only for newly allocated bands, then the boosting incurs insignificant and acceptable interference to the PNs. However, the boosting can violate non-interference requirements of the network and increase overhead to achieve a desired reliability.
Another method uses a transform domain communication network, and a conventional contention scheme for access signaling of a network with a base station, Han, et al., “A Spectrum Exchange Mechanism in Cognitive Radio Contexts,” IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1-5, September 2006.
Another method uses a probabilistic approach for collaborative detection under soft and hard information combining strategies, Visotsky et al., “On Collaborative Detection of TV Transmissions in Support of Dynamic Spectrum Sharing,” First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), pp. 338-345, November 2005.
None of the above methods consider the joint problem of detection and signaling to determine the overhead associated with spectrum exchange mechanism.
One method describes an analytical model of CR MAC with two types of channel sensing, Su et al., “Cognitive Radio Based Multi-Channel MAC Protocols for Wireless Ad Hoc Networks,” IEEE Global Telecommunications Conference (GLOBECOM), pp. 4857-4861, November 2007. However, that MAC mechanism requires exact synchronization a small time scale.
Several methods minimize the interference with or by PNs by using the DCC, Azarnasab et al., “Filterbank Multicarrier and Multicarrier CDMA for Cognitive Radio Systems,” IEEE CrownCom, August 2007, Cabric et al., “Implementation Issues in Spectrum Sensing for Cognitive Radios,” Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 772-776, November 2004, Mishra et al., “Cooperative Sensing Among Cognitive Radios,” IEEE International Conference on Communications (ICC), vol. 4, pp. 1658-1663, June 2006, and Cabric et al., “A Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum,” 14th IST Mobile and Wireless Communications Summit, June 2005.